鸟枪法蛋白质组学数据的计算分析

Computational analysis of shotgun proteomics data.

作者信息

MacCoss Michael J

机构信息

Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, K307, Box 357730, Seattle, WA 98195-7730, USA.

出版信息

Curr Opin Chem Biol. 2005 Feb;9(1):88-94. doi: 10.1016/j.cbpa.2004.12.010.

DOI:10.1016/j.cbpa.2004.12.010

PMID:15701459

Abstract

Proteomics technology is progressing at an incredible rate. The latest generation of tandem mass spectrometers can now acquire tens of thousands of fragmentation spectra in a matter of hours. Furthermore, quantitative proteomics methods have been developed that incorporate a stable isotope-labeled internal standard for every peptide within a complex protein mixture for the measurement of relative protein abundances. These developments have opened the doors for 'shotgun' proteomics, yet have also placed a burden on the computational approaches that manage the data. With each new method that is developed, the quantity of data that can be derived from a single experiment increases. To deal with this increase, new computational approaches are being developed to manage the data and assess false positives. This review discusses current approaches for analyzing proteomics data by mass spectrometry and identifies present computational limitations and bottlenecks.

摘要

蛋白质组学技术正以惊人的速度发展。最新一代的串联质谱仪现在能够在数小时内获取数以万计的碎片谱。此外，已经开发出定量蛋白质组学方法，该方法为复杂蛋白质混合物中的每个肽段引入稳定同位素标记的内标，用于测量相对蛋白质丰度。这些进展为“鸟枪法”蛋白质组学打开了大门，但也给管理数据的计算方法带来了负担。随着每一种新方法的开发，从单个实验中可获得的数据量都在增加。为了应对这种增长，正在开发新的计算方法来管理数据并评估假阳性。本综述讨论了目前通过质谱分析蛋白质组学数据的方法，并确定了当前的计算限制和瓶颈。